Library & Dataset

Using OLR

Inspect Dataset Using Training and Validation

OLR Equations

Using all variables

Inspect Dataset Using Training and Validation

Training set divide in the Diagnostic_based.on.Harell.Rmd file

vclust <- varclus (~angle+brick+wood+mixed+ density+EN +TC + TC_mature_soil + TC_saprolite_soil +  TC_weath_rock  + TC_unstable_structure  + T_construction  + spring +  landfill + garbage  + crack + leaning_wall + scars + downward_floor + tilted + fracture + conc_rainfall + wastewater + leak + septic_tank  + tree + ground_veg + deforestation + banana + drainage , data=train.data)

# took out density since training has 0 d4 and it was not allowing do the plot

p <- plot(vclust)

par(mfrow=c(6,5))
plot.xmean.ordinaly (risk~angle+brick+wood+mixed+ density+EN +TC + TC_mature_soil + TC_saprolite_soil +  TC_weath_rock  + TC_unstable_structure  + T_construction  + spring +  landfill + garbage  + crack + leaning_wall + scars + downward_floor + tilted + fracture + conc_rainfall + wastewater + leak + septic_tank  + tree + ground_veg + deforestation + banana + drainage, data=train.data, cr=TRUE , subn=FALSE)

#angle + building+density+EN +TC + TC_mature_Soil + TC_saprolito +  TC_weath_rock + TC_rock + TC_geol_desfav + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + DepTaludeAterro + aterro + lixo + entulho + crack + belly_wall + scars + drawback + tilted + fracture + conc_rainfall_water + wastewater + leak + septic_tank + drainage + tree + ground_veg + deforestation + banana

Diagnostic 2: Proportion (-5% of one of the parameters based on what is expected. Since some parameters have 2 predictors, others 5)

#library(plyr)
brick <- count(train.data$brick) %>% 
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "brick")

wood <- count(train.data$wood) %>% 
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "wood")

mixed <- count(train.data$mixed) %>% 
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "mixed")

TC_mature_soil <- count(train.data$TC_mature_soil) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_mature_soil")

T_construction  <- count(train.data$T_construction ) %>%
  mutate ("Percentage"=(freq/265)*100) %>%
  mutate("Classifier" = "T_construction ")

spring <- count(train.data$spring) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "spring")

landfill <- count(train.data$landfill) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "landfill")

garbage <- count(train.data$garbage) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "garbage")

crack <- count(train.data$crack) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "crack")

leaning_wall <- count(train.data$leaning_wall) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "leaning_wall")

scars <- count(train.data$scars) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "DepTaludeAterro")

downward_floor <- count(train.data$downward_floor) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "scars")

tilted <- count(train.data$tilted) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "tilted")

conc_rainfall <- count(train.data$conc_rainfall) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "conc_rainfall")

wastewater <- count(train.data$wastewater) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "wastewater")

leak <- count(train.data$leak) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "conc_rainfall_water")

septic_tank <- count(train.data$septic_tank) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "septic_tank")

angle <- count(train.data$angle) # angle A less than 5% but the rest are okay (3,50, 91, 277, 109) Expected=106
angle <- angle %>%
  mutate("Percentage"=(freq/106)*100)%>%
  mutate("Classifier" = "angle")

EN <- count(train.data$EN) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "EN")

TC <- count(train.data$TC)  %>%
  mutate ("Percentage"=(freq/265)*100) %>%
  mutate("Classifier" = "TC")

TC_saprolite_soil  <- count(train.data$TC_saprolite_soil )  %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_saprolite_soil ")

banana <- count(train.data$banana) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "banana")

drainage <- count(train.data$drainage) %>%
  mutate ("Percentage"=(freq/176.7)*100)%>%
  mutate("Classifier" = "drainage")

deforestation <- count(train.data$deforestation) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "deforestation")

TC_unstable_structure  <- count(train.data$TC_unstable_structure ) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_unstable_structure ")


tree <- count(train.data$tree) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "tree")

ground_veg <- count(train.data$ground_veg) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "ground_veg")


density <- count(train.data$density)  %>% #(79, 415, 36) # d4 =0 
  mutate ("Percentage"=(freq/132.5)*100)%>%
  mutate("Classifier" = "density")

TC_weath_rock  <- count(train.data$TC_weath_rock ) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_weath_rock ")

fracture <- count(train.data$fracture) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "fracture")









df <- rbind(brick, wood, mixed, TC_mature_soil, T_construction, spring, landfill, garbage, crack, leaning_wall, scars, downward_floor, tilted, conc_rainfall, wastewater, leak, septic_tank, angle, EN, TC, TC_saprolite_soil,  banana, drainage, deforestation, TC_unstable_structure, tree, ground_veg,density, TC_weath_rock, fracture)

df

##        x freq  Percentage             Classifier
## 1  FALSE   32  12.0754717                  brick
## 2   TRUE  498 187.9245283                  brick
## 3  FALSE  454 171.3207547                   wood
## 4   TRUE   76  28.6792453                   wood
## 5  FALSE  491 185.2830189                  mixed
## 6   TRUE   39  14.7169811                  mixed
## 7  FALSE  251  94.7169811         TC_mature_soil
## 8   TRUE  279 105.2830189         TC_mature_soil
## 9  FALSE  213  80.3773585        T_construction 
## 10  TRUE  317 119.6226415        T_construction 
## 11 FALSE  510 192.4528302                 spring
## 12  TRUE   20   7.5471698                 spring
## 13 FALSE  326 123.0188679               landfill
## 14  TRUE  204  76.9811321               landfill
## 15 FALSE  347 130.9433962                garbage
## 16  TRUE  183  69.0566038                garbage
## 17 FALSE  445 167.9245283                  crack
## 18  TRUE   85  32.0754717                  crack
## 19 FALSE  500 188.6792453           leaning_wall
## 20  TRUE   30  11.3207547           leaning_wall
## 21 FALSE  321 121.1320755        DepTaludeAterro
## 22  TRUE  209  78.8679245        DepTaludeAterro
## 23 FALSE  468 176.6037736                  scars
## 24  TRUE   62  23.3962264                  scars
## 25 FALSE  426 160.7547170                 tilted
## 26  TRUE  104  39.2452830                 tilted
## 27 FALSE   14   5.2830189          conc_rainfall
## 28  TRUE  516 194.7169811          conc_rainfall
## 29 FALSE  206  77.7358491             wastewater
## 30  TRUE  324 122.2641509             wastewater
## 31 FALSE  337 127.1698113    conc_rainfall_water
## 32  TRUE  193  72.8301887    conc_rainfall_water
## 33 FALSE  525 198.1132075            septic_tank
## 34  TRUE    5   1.8867925            septic_tank
## 35     C   32  30.1886792                  angle
## 36     D  118 111.3207547                  angle
## 37     E  380 358.4905660                  angle
## 38 FALSE  349 131.6981132                     EN
## 39  TRUE  181  68.3018868                     EN
## 40 FALSE   26   9.8113208                     TC
## 41  TRUE  504 190.1886792                     TC
## 42 FALSE  446 168.3018868     TC_saprolite_soil 
## 43  TRUE   84  31.6981132     TC_saprolite_soil 
## 44 FALSE  361 136.2264151                 banana
## 45  TRUE  169  63.7735849                 banana
## 46     Y   62  35.0877193               drainage
## 47     P  238 134.6915676               drainage
## 48     N  230 130.1641200               drainage
## 49 FALSE  492 185.6603774          deforestation
## 50  TRUE   38  14.3396226          deforestation
## 51 FALSE  517 195.0943396 TC_unstable_structure 
## 52  TRUE   13   4.9056604 TC_unstable_structure 
## 53 FALSE  215  81.1320755                   tree
## 54  TRUE  315 118.8679245                   tree
## 55 FALSE  147  55.4716981             ground_veg
## 56  TRUE  383 144.5283019             ground_veg
## 57    d1   69  52.0754717                density
## 58    d2  422 318.4905660                density
## 59    d3   39  29.4339623                density
## 60 FALSE  520 196.2264151         TC_weath_rock 
## 61  TRUE   10   3.7735849         TC_weath_rock 
## 62 FALSE  529 199.6226415               fracture
## 63  TRUE    1   0.3773585               fracture

TC_weath_rock, TC_rock_TC_geol_desf, fracture, TC_rock

OF: angle (slope angle), building (building material), EN (natural slope), TC_saprolito (cut slope with saprolite), Taterro (slope with landfill), DepEncostaNatural (natural slope with construction material), DepTaludeAterro (slope with landfill and construction material), DepTaludeCorte (cut slope with construction material), landfill, garbage, construction_deposit (construction material), crack, leaning_wall, scars, downward_floor, tilted, conc_rainfall_water (concentration of rainfall water), wastewater, leak, drainage
MOF:TC_mature_soil (slope cut with soil), density, TC (slope cut), - - TC_geol_desfav (cut slope with geology favorable to instability), tree, ground_veg (ground vegetation), deforestation, banana
NOF: TC_weath_rock (cut slope with weathering rock), TC_rock (cut slope in bedrock), fracture, septic_tank
GP:all classifiers minus BP classifiers
BP: TC_weath_rock (True N= 11 , False N= 529), TC_geol_desf (True N= 11 , False N= 529), fracture (True N=1 , False N=529), TC_rock (True N=1 , False N=529 ), septic_tank (True N=4 , False N=526 )

Equation 1

consider all parameter okay from first diagnostic:

f1 <- lrm(risk ~ building + EN + TC_saprolito + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + landfill + garbage + construction_deposit + crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + leak + tree + ground_veg + banana , data=train.data, x=TRUE , y=TRUE)

# Equation 1

eq_OLR_01 <- polr(risk ~ brick+ wood+ EN +  TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil, data= train.data
           ,  method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_01))



p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                             Value Std. Error    t value      p value
## brickTRUE             -0.74369444  0.4575882 -1.6252483 5.205483e-02
## woodTRUE               0.93103246  0.3278246  2.8400327 2.255445e-03
## ENTRUE                 0.76065809  0.3946457  1.9274453 2.696208e-02
## TC_mature_soilTRUE     0.50446573  0.2181304  2.3126795 1.037013e-02
## T_constructionTRUE     0.45604364  0.3658514  1.2465271 1.062855e-01
## springTRUE            -0.32689239  0.6087008 -0.5370329 2.956224e-01
## landfillTRUE           0.11306814  0.3286765  0.3440104 3.654192e-01
## leakTRUE              -0.17095573  0.2331696 -0.7331821 2.317237e-01
## garbageTRUE            0.15937798  0.2937571  0.5425502 2.937198e-01
## crackTRUE              2.07693093  0.3333863  6.2298031 2.335109e-10
## leaning_wallTRUE       1.74722530  0.5296430  3.2988734 4.853684e-04
## scarsTRUE              3.62063572  0.3319293 10.9078530 5.286192e-28
## downward_floorTRUE     1.38858260  0.3683330  3.7699112 8.165281e-05
## tiltedTRUE             1.13414341  0.3141672  3.6099997 1.530987e-04
## septic_tankTRUE        0.16805216  1.1063295  0.1519006 4.396327e-01
## conc_rainfallTRUE      1.10939042  0.5805358  1.9109769 2.800378e-02
## wastewaterTRUE         0.69843609  0.2350376  2.9715927 1.481297e-03
## ground_vegTRUE         0.88362334  0.2539747  3.4791792 2.514761e-04
## angleD                 0.36800398  0.4674107  0.7873247 2.155459e-01
## angleE                 0.98179588  0.5537966  1.7728457 3.812714e-02
## TC_saprolite_soilTRUE  0.06527116  0.2903751  0.2247822 4.110744e-01
## R1|R2                  0.84944226  0.9201676  0.9231387 1.779675e-01
## R2|R3                  4.96915322  0.9671494  5.1379376 1.388851e-07
## R3|R4                  9.94969481  1.0655084  9.3379790 4.909521e-21

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ======================================================
##                       Value Std. Error t value p value
## ------------------------------------------------------
## brickTRUE             -0.74    0.46     -1.63   0.05  
## woodTRUE              0.93     0.33     2.84    0.002 
## ENTRUE                0.76     0.39     1.93    0.03  
## TC_mature_soilTRUE    0.50     0.22     2.31    0.01  
## T_constructionTRUE    0.46     0.37     1.25    0.11  
## springTRUE            -0.33    0.61     -0.54   0.30  
## landfillTRUE          0.11     0.33     0.34    0.37  
## leakTRUE              -0.17    0.23     -0.73   0.23  
## garbageTRUE           0.16     0.29     0.54    0.29  
## crackTRUE             2.08     0.33     6.23      0   
## leaning_wallTRUE      1.75     0.53     3.30   0.0005 
## scarsTRUE             3.62     0.33     10.91     0   
## downward_floorTRUE    1.39     0.37     3.77   0.0001 
## tiltedTRUE            1.13     0.31     3.61   0.0002 
## septic_tankTRUE       0.17     1.11     0.15    0.44  
## conc_rainfallTRUE     1.11     0.58     1.91    0.03  
## wastewaterTRUE        0.70     0.24     2.97    0.001 
## ground_vegTRUE        0.88     0.25     3.48   0.0003 
## angleD                0.37     0.47     0.79    0.22  
## angleE                0.98     0.55     1.77    0.04  
## TC_saprolite_soilTRUE 0.07     0.29     0.22    0.41  
## R1| R2                0.85     0.92     0.92    0.18  
## R2| R3                4.97     0.97     5.14   0.0000 
## R3| R4                9.95     1.07     9.34      0   
## ------------------------------------------------------

less p-value = 0.10 (TC_saprolitoTRUE,TaterroTRUE, DepTaludeAterroTRUE,DepTaludeAterroTRUE,landfillTRUE, construction_depositTRUE, leakTRUE)

par(mfrow=c(5,4))
plot.xmean.ordinaly (risk~ brick+ wood+ EN +  TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil
          ,data=train.data, cr=TRUE , subn=FALSE ,  cex.lab=1.5, cex.axis=2, cex.sub=2, cex.main=2)

Creating function with four level

Equation 1

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ EN +  TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +-----------------+---+---+----+--------+------------+-----------+
## |                 |   |N  |y>=1|y>=2    |y>=3        |y>=4       |
## +-----------------+---+---+----+--------+------------+-----------+
## |brick            |No | 32|Inf |2.708050| 1.272965676|-0.78845736|
## |                 |Yes|497|Inf |2.307020|-0.076496033|-2.02405640|
## +-----------------+---+---+----+--------+------------+-----------+
## |wood             |No |453|Inf |2.204605|-0.190418767|-2.25495704|
## |                 |Yes| 76|Inf |3.610918| 1.244324100|-0.71294981|
## +-----------------+---+---+----+--------+------------+-----------+
## |EN               |No |348|Inf |1.932838|-0.492476485|-2.39789527|
## |                 |Yes|181|Inf |4.083171| 0.990981624|-1.29226541|
## +-----------------+---+---+----+--------+------------+-----------+
## |TC_mature_soil   |No |251|Inf |1.959640|-0.248279525|-2.15800386|
## |                 |Yes|278|Inf |2.795756| 0.216671037|-1.72616219|
## +-----------------+---+---+----+--------+------------+-----------+
## |T_construction   |No |213|Inf |1.559566|-0.867500568|-2.81839826|
## |                 |Yes|316|Inf |3.421000| 0.558372780|-1.53532994|
## +-----------------+---+---+----+--------+------------+-----------+
## |spring           |No |509|Inf |2.285417|-0.043228735|-2.03171014|
## |                 |Yes| 20|Inf |     Inf| 1.098612289|-0.20067070|
## +-----------------+---+---+----+--------+------------+-----------+
## |landfill         |No |325|Inf |1.828127|-0.509184934|-2.52905643|
## |                 |Yes|204|Inf |4.615121| 0.828692673|-1.29098418|
## +-----------------+---+---+----+--------+------------+-----------+
## |leak             |No |336|Inf |2.001480|-0.251314428|-2.32238772|
## |                 |Yes|193|Inf |3.279837| 0.431440595|-1.40583896|
## +-----------------+---+---+----+--------+------------+-----------+
## |garbage          |No |347|Inf |2.066538|-0.272568435|-2.28683674|
## |                 |Yes|182|Inf |3.079614| 0.516690743|-1.40008768|
## +-----------------+---+---+----+--------+------------+-----------+
## |crack            |No |444|Inf |2.157811|-0.345745873|-2.73724936|
## |                 |Yes| 85|Inf |4.430817| 2.772588722|-0.07061757|
## +-----------------+---+---+----+--------+------------+-----------+
## |leaning_wall     |No |499|Inf |2.263535|-0.108322227|-2.15131488|
## |                 |Yes| 30|Inf |     Inf| 2.639057330| 0.13353139|
## +-----------------+---+---+----+--------+------------+-----------+
## |scars            |No |320|Inf |1.784487|-1.366876275|-4.36944785|
## |                 |Yes|209|Inf |5.337538| 2.990719732|-0.81785066|
## +-----------------+---+---+----+--------+------------+-----------+
## |downward_floor   |No |467|Inf |2.190107|-0.262743124|-2.26318255|
## |                 |Yes| 62|Inf |     Inf| 4.110873864|-0.45953233|
## +-----------------+---+---+----+--------+------------+-----------+
## |tilted           |No |425|Inf |2.108895|-0.444840273|-2.50807371|
## |                 |Yes|104|Inf |4.634729| 2.793208009|-0.63598877|
## +-----------------+---+---+----+--------+------------+-----------+
## |septic_tank      |No |524|Inf |2.317369|-0.007633625|-1.91999077|
## |                 |Yes|  5|Inf |     Inf| 0.405465108|-1.38629436|
## +-----------------+---+---+----+--------+------------+-----------+
## |conc_rainfall    |No | 14|Inf |0.000000|        -Inf|       -Inf|
## |                 |Yes|515|Inf |2.474435| 0.050496164|-1.88305089|
## +-----------------+---+---+----+--------+------------+-----------+
## |wastewater       |No |206|Inf |1.693319|-0.433864583|-2.78295151|
## |                 |Yes|323|Inf |3.022050| 0.267843730|-1.56189697|
## +-----------------+---+---+----+--------+------------+-----------+
## |ground_veg       |No |147|Inf |1.279196|-1.319602987|-2.73002911|
## |                 |Yes|382|Inf |3.197312| 0.447092148|-1.70011488|
## +-----------------+---+---+----+--------+------------+-----------+
## |angle            |C  | 32|Inf |     Inf|-0.379489622|-3.43398720|
## |                 |D  |118|Inf |3.646320| 1.167605160|-1.31372367|
## |                 |E  |379|Inf |2.029941|-0.303090698|-2.08241331|
## +-----------------+---+---+----+--------+------------+-----------+
## |TC_saprolite_soil|No |445|Inf |2.261267|-0.040454955|-2.00096993|
## |                 |Yes| 84|Inf |2.760010| 0.191055237|-1.52605630|
## +-----------------+---+---+----+--------+------------+-----------+
## |Overall          |   |529|Inf |2.327797|-0.003780723|-1.91389034|
## +-----------------+---+---+----+--------+------------+-----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=1, cex.axis=1, cex.sub=1)

Equation 2

parameters okay and so/so
porportion
excluded TC_geol_desf

f2 <- lrm(risk ~ angle + building + EN + TC_saprolito + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + landfill + garbage + construction_deposit + crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + leak + drainage + TC_mature_Soil + density + TC + tree +ground_veg + deforestation + banana , data=train.data, x=TRUE , y=TRUE)

      stargazer(anova(f2), type="text", style="default")

eq_OLR_02 <- polr(risk ~ brick+ wood+ EN+  TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation,
                  
                 data= train.data
           ,  method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_02))








p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                             Value Std. Error     t value      p value
## brickTRUE             -0.95863248  0.5455880 -1.75706305 3.945353e-02
## woodTRUE               0.70992717  0.3450795  2.05728556 1.982938e-02
## ENTRUE                 0.48429561  0.4121252  1.17511759 1.199738e-01
## TC_mature_soilTRUE     0.62338974  0.2332306  2.67284669 3.760531e-03
## T_constructionTRUE     0.59193296  0.3732852  1.58573900 5.639926e-02
## landfillTRUE           0.08469955  0.3324418  0.25478004 3.994465e-01
## leakTRUE              -0.22645913  0.2367195 -0.95665613 1.693704e-01
## garbageTRUE            0.10750341  0.3003836  0.35788704 3.602139e-01
## crackTRUE              2.09890938  0.3369541  6.22906678 2.346109e-10
## leaning_wallTRUE       1.83094078  0.5438887  3.36638854 3.807968e-04
## treeTRUE              -0.28360337  0.2429547 -1.16730966 1.215427e-01
## downward_floorTRUE     1.29265791  0.3702181  3.49161144 2.400581e-04
## tiltedTRUE             1.07694567  0.3179161  3.38751521 3.526440e-04
## ground_vegTRUE         0.78618525  0.2810682  2.79713306 2.577915e-03
## scarsTRUE              3.63580275  0.3386395 10.73649936 3.429814e-27
## mixedTRUE             -0.45119693  0.5076667 -0.88876600 1.870644e-01
## conc_rainfallTRUE      0.61011856  0.6204584  0.98333515 1.627213e-01
## wastewaterTRUE         0.52360366  0.2455115  2.13270512 1.647446e-02
## angleD                 0.01367017  0.4768340  0.02866862 4.885644e-01
## angleE                 0.70460344  0.5615076  1.25484231 1.047680e-01
## bananaTRUE             0.32181674  0.2603309  1.23618353 1.081952e-01
## drainage.L             0.97403309  0.2865389  3.39930499 3.377867e-04
## drainage.Q            -0.11203751  0.1880318 -0.59584350 2.756399e-01
## TC_saprolite_soilTRUE  0.09849279  0.3025284  0.32556547 3.723766e-01
## TCTRUE                -1.37418458  0.5494866 -2.50085195 6.194748e-03
## deforestationTRUE      0.33230984  0.4089782  0.81253676 2.082419e-01
## R1|R2                 -1.64285793  1.1829795 -1.38874594 8.245501e-02
## R2|R3                  2.71163442  1.1889701  2.28065813 1.128434e-02
## R3|R4                  7.71187953  1.2574260  6.13306810 4.310011e-10

stargazer((ctable), type="text", style="default", digits=2)

## 
## ======================================================
##                       Value Std. Error t value p value
## ------------------------------------------------------
## brickTRUE             -0.96    0.55     -1.76   0.04  
## woodTRUE              0.71     0.35     2.06    0.02  
## ENTRUE                0.48     0.41     1.18    0.12  
## TC_mature_soilTRUE    0.62     0.23     2.67    0.004 
## T_constructionTRUE    0.59     0.37     1.59    0.06  
## landfillTRUE          0.08     0.33     0.25    0.40  
## leakTRUE              -0.23    0.24     -0.96   0.17  
## garbageTRUE           0.11     0.30     0.36    0.36  
## crackTRUE             2.10     0.34     6.23      0   
## leaning_wallTRUE      1.83     0.54     3.37   0.0004 
## treeTRUE              -0.28    0.24     -1.17   0.12  
## downward_floorTRUE    1.29     0.37     3.49   0.0002 
## tiltedTRUE            1.08     0.32     3.39   0.0004 
## ground_vegTRUE        0.79     0.28     2.80    0.003 
## scarsTRUE             3.64     0.34     10.74     0   
## mixedTRUE             -0.45    0.51     -0.89   0.19  
## conc_rainfallTRUE     0.61     0.62     0.98    0.16  
## wastewaterTRUE        0.52     0.25     2.13    0.02  
## angleD                0.01     0.48     0.03    0.49  
## angleE                0.70     0.56     1.25    0.10  
## bananaTRUE            0.32     0.26     1.24    0.11  
## drainage.L            0.97     0.29     3.40   0.0003 
## drainage.Q            -0.11    0.19     -0.60   0.28  
## TC_saprolite_soilTRUE 0.10     0.30     0.33    0.37  
## TCTRUE                -1.37    0.55     -2.50   0.01  
## deforestationTRUE     0.33     0.41     0.81    0.21  
## R1| R2                -1.64    1.18     -1.39   0.08  
## R2| R3                2.71     1.19     2.28    0.01  
## R3| R4                7.71     1.26     6.13      0   
## ------------------------------------------------------

par(mfrow=c(6,4))
plot.xmean.ordinaly (risk~ brick+ wood+ EN+  TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation
          ,data=train.data, cr=TRUE , subn=FALSE ,  cex.lab=1.5, cex.axis=4, cex.sub=4, cex.main=4)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ EN+  TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation,data=train.data
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +-----------------+---+---+----+---------+------------+-----------+
## |                 |   |N  |y>=1|y>=2     |y>=3        |y>=4       |
## +-----------------+---+---+----+---------+------------+-----------+
## |brick            |No | 32|Inf |2.7080502| 1.272965676|-0.78845736|
## |                 |Yes|497|Inf |2.3070197|-0.076496033|-2.02405640|
## +-----------------+---+---+----+---------+------------+-----------+
## |wood             |No |453|Inf |2.2046047|-0.190418767|-2.25495704|
## |                 |Yes| 76|Inf |3.6109179| 1.244324100|-0.71294981|
## +-----------------+---+---+----+---------+------------+-----------+
## |EN               |No |348|Inf |1.9328381|-0.492476485|-2.39789527|
## |                 |Yes|181|Inf |4.0831713| 0.990981624|-1.29226541|
## +-----------------+---+---+----+---------+------------+-----------+
## |TC_mature_soil   |No |251|Inf |1.9596403|-0.248279525|-2.15800386|
## |                 |Yes|278|Inf |2.7957558| 0.216671037|-1.72616219|
## +-----------------+---+---+----+---------+------------+-----------+
## |T_construction   |No |213|Inf |1.5595661|-0.867500568|-2.81839826|
## |                 |Yes|316|Inf |3.4210000| 0.558372780|-1.53532994|
## +-----------------+---+---+----+---------+------------+-----------+
## |landfill         |No |325|Inf |1.8281271|-0.509184934|-2.52905643|
## |                 |Yes|204|Inf |4.6151205| 0.828692673|-1.29098418|
## +-----------------+---+---+----+---------+------------+-----------+
## |leak             |No |336|Inf |2.0014800|-0.251314428|-2.32238772|
## |                 |Yes|193|Inf |3.2798365| 0.431440595|-1.40583896|
## +-----------------+---+---+----+---------+------------+-----------+
## |garbage          |No |347|Inf |2.0665381|-0.272568435|-2.28683674|
## |                 |Yes|182|Inf |3.0796138| 0.516690743|-1.40008768|
## +-----------------+---+---+----+---------+------------+-----------+
## |crack            |No |444|Inf |2.1578106|-0.345745873|-2.73724936|
## |                 |Yes| 85|Inf |4.4308168| 2.772588722|-0.07061757|
## +-----------------+---+---+----+---------+------------+-----------+
## |leaning_wall     |No |499|Inf |2.2635346|-0.108322227|-2.15131488|
## |                 |Yes| 30|Inf |      Inf| 2.639057330| 0.13353139|
## +-----------------+---+---+----+---------+------------+-----------+
## |tree             |No |214|Inf |1.7754989|-0.515813165|-2.16645292|
## |                 |Yes|315|Inf |2.9278549| 0.339738434|-1.76606998|
## +-----------------+---+---+----+---------+------------+-----------+
## |downward_floor   |No |467|Inf |2.1901071|-0.262743124|-2.26318255|
## |                 |Yes| 62|Inf |      Inf| 4.110873864|-0.45953233|
## +-----------------+---+---+----+---------+------------+-----------+
## |tilted           |No |425|Inf |2.1088948|-0.444840273|-2.50807371|
## |                 |Yes|104|Inf |4.6347290| 2.793208009|-0.63598877|
## +-----------------+---+---+----+---------+------------+-----------+
## |ground_veg       |No |147|Inf |1.2791962|-1.319602987|-2.73002911|
## |                 |Yes|382|Inf |3.1973116| 0.447092148|-1.70011488|
## +-----------------+---+---+----+---------+------------+-----------+
## |scars            |No |320|Inf |1.7844867|-1.366876275|-4.36944785|
## |                 |Yes|209|Inf |5.3375381| 2.990719732|-0.81785066|
## +-----------------+---+---+----+---------+------------+-----------+
## |mixed            |No |490|Inf |2.2914118|-0.073502462|-2.00798258|
## |                 |Yes| 39|Inf |2.9177707| 0.934309237|-1.06471074|
## +-----------------+---+---+----+---------+------------+-----------+
## |conc_rainfall    |No | 14|Inf |0.0000000|        -Inf|       -Inf|
## |                 |Yes|515|Inf |2.4744353| 0.050496164|-1.88305089|
## +-----------------+---+---+----+---------+------------+-----------+
## |wastewater       |No |206|Inf |1.6933194|-0.433864583|-2.78295151|
## |                 |Yes|323|Inf |3.0220496| 0.267843730|-1.56189697|
## +-----------------+---+---+----+---------+------------+-----------+
## |angle            |C  | 32|Inf |      Inf|-0.379489622|-3.43398720|
## |                 |D  |118|Inf |3.6463198| 1.167605160|-1.31372367|
## |                 |E  |379|Inf |2.0299409|-0.303090698|-2.08241331|
## +-----------------+---+---+----+---------+------------+-----------+
## |banana           |No |360|Inf |1.9977017|-0.279584862|-2.19722458|
## |                 |Yes|169|Inf |3.7196511| 0.597003320|-1.45424502|
## +-----------------+---+---+----+---------+------------+-----------+
## |drainage         |Y  | 62|Inf |0.7419373|-2.061423036|       -Inf|
## |                 |P  |237|Inf |2.3841651|-0.508575904|-2.69462718|
## |                 |N  |230|Inf |3.4612616| 0.996829594|-1.20585782|
## +-----------------+---+---+----+---------+------------+-----------+
## |TC_saprolite_soil|No |445|Inf |2.2612669|-0.040454955|-2.00096993|
## |                 |Yes| 84|Inf |2.7600099| 0.191055237|-1.52605630|
## +-----------------+---+---+----+---------+------------+-----------+
## |TC               |No | 26|Inf |      Inf| 1.203972804|-1.20397280|
## |                 |Yes|503|Inf |2.2723452|-0.059659836|-1.96191049|
## +-----------------+---+---+----+---------+------------+-----------+
## |deforestation    |No |491|Inf |2.3435931| 0.052965536|-1.89790093|
## |                 |Yes| 38|Inf |2.1400662|-0.773189888|-2.14006616|
## +-----------------+---+---+----+---------+------------+-----------+
## |Overall          |   |529|Inf |2.3277965|-0.003780723|-1.91389034|
## +-----------------+---+---+----+---------+------------+-----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=1, cex.axis=2, cex.sub=1)

Equation 3

parameters okay and so/so
porportion
p-value based equation 2 > 0.5

f3 <- lrm(risk ~ angle +building + EN + DepTaludeAterro+ DepTaludeCorte+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall_water+ wastewater+ tree + TC , data=train.data, x=TRUE , y=TRUE) stargazer(anova(f3), type=“text”, style=“default”)

# x=TRUE, y=TRUE used by resid() below 

eq_OLR_03 <- polr(risk ~ wood+  TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage, data=train.data
           ,  method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_03))


p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                          Value Std. Error     t value      p value
## woodTRUE            0.84125762  0.3235614  2.59999340 4.661278e-03
## TC_mature_soilTRUE  0.45997878  0.2181854  2.10820115 1.750680e-02
## T_constructionTRUE  0.70164945  0.2931393  2.39357028 8.342642e-03
## landfillTRUE       -0.08782343  0.2926312 -0.30011645 3.820442e-01
## crackTRUE           2.04329525  0.3275123  6.23883565 2.204199e-10
## leaning_wallTRUE    1.88862216  0.5354252  3.52733172 2.098852e-04
## treeTRUE           -0.29800037  0.2320711 -1.28409057 9.955513e-02
## downward_floorTRUE  1.19189772  0.3528581  3.37783824 3.652902e-04
## tiltedTRUE          1.07269191  0.3099721  3.46060791 2.694786e-04
## ground_vegTRUE      0.81136372  0.2696212  3.00927263 1.309370e-03
## scarsTRUE           3.56885323  0.3322471 10.74156300 3.246780e-27
## conc_rainfallTRUE   0.71733097  0.6108912  1.17423684 1.201501e-01
## wastewaterTRUE      0.46337270  0.2340014  1.98021378 2.383976e-02
## bananaTRUE          0.24154605  0.2467598  0.97887096 1.638219e-01
## drainage.L          0.96556502  0.2784342  3.46783913 2.623306e-04
## drainage.Q         -0.11231798  0.1852456 -0.60631928 2.721514e-01
## R1|R2              -0.01420245  0.5935702 -0.02392716 4.904554e-01
## R2|R3               4.23561369  0.6473844  6.54265659 3.021775e-11
## R3|R4               9.12524842  0.7623660 11.96964284 2.562438e-33

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## woodTRUE           0.84     0.32     2.60    0.005 
## TC_mature_soilTRUE 0.46     0.22     2.11    0.02  
## T_constructionTRUE 0.70     0.29     2.39    0.01  
## landfillTRUE       -0.09    0.29     -0.30   0.38  
## crackTRUE          2.04     0.33     6.24      0   
## leaning_wallTRUE   1.89     0.54     3.53   0.0002 
## treeTRUE           -0.30    0.23     -1.28   0.10  
## downward_floorTRUE 1.19     0.35     3.38   0.0004 
## tiltedTRUE         1.07     0.31     3.46   0.0003 
## ground_vegTRUE     0.81     0.27     3.01    0.001 
## scarsTRUE          3.57     0.33     10.74     0   
## conc_rainfallTRUE  0.72     0.61     1.17    0.12  
## wastewaterTRUE     0.46     0.23     1.98    0.02  
## bananaTRUE         0.24     0.25     0.98    0.16  
## drainage.L         0.97     0.28     3.47   0.0003 
## drainage.Q         -0.11    0.19     -0.61   0.27  
## R1| R2             -0.01    0.59     -0.02   0.49  
## R2| R3             4.24     0.65     6.54      0   
## R3| R4             9.13     0.76     11.97     0   
## ---------------------------------------------------

#print (f3 , latex =TRUE , coefs =5)

par(mfrow=c(3,5))
plot.xmean.ordinaly (risk ~  wood+  TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage,,
          data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~wood+  TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+---------+------------+-----------+
## |              |   |N  |y>=1|y>=2     |y>=3        |y>=4       |
## +--------------+---+---+----+---------+------------+-----------+
## |wood          |No |453|Inf |2.2046047|-0.190418767|-2.25495704|
## |              |Yes| 76|Inf |3.6109179| 1.244324100|-0.71294981|
## +--------------+---+---+----+---------+------------+-----------+
## |TC_mature_soil|No |251|Inf |1.9596403|-0.248279525|-2.15800386|
## |              |Yes|278|Inf |2.7957558| 0.216671037|-1.72616219|
## +--------------+---+---+----+---------+------------+-----------+
## |T_construction|No |213|Inf |1.5595661|-0.867500568|-2.81839826|
## |              |Yes|316|Inf |3.4210000| 0.558372780|-1.53532994|
## +--------------+---+---+----+---------+------------+-----------+
## |landfill      |No |325|Inf |1.8281271|-0.509184934|-2.52905643|
## |              |Yes|204|Inf |4.6151205| 0.828692673|-1.29098418|
## +--------------+---+---+----+---------+------------+-----------+
## |crack         |No |444|Inf |2.1578106|-0.345745873|-2.73724936|
## |              |Yes| 85|Inf |4.4308168| 2.772588722|-0.07061757|
## +--------------+---+---+----+---------+------------+-----------+
## |leaning_wall  |No |499|Inf |2.2635346|-0.108322227|-2.15131488|
## |              |Yes| 30|Inf |      Inf| 2.639057330| 0.13353139|
## +--------------+---+---+----+---------+------------+-----------+
## |tree          |No |214|Inf |1.7754989|-0.515813165|-2.16645292|
## |              |Yes|315|Inf |2.9278549| 0.339738434|-1.76606998|
## +--------------+---+---+----+---------+------------+-----------+
## |downward_floor|No |467|Inf |2.1901071|-0.262743124|-2.26318255|
## |              |Yes| 62|Inf |      Inf| 4.110873864|-0.45953233|
## +--------------+---+---+----+---------+------------+-----------+
## |tilted        |No |425|Inf |2.1088948|-0.444840273|-2.50807371|
## |              |Yes|104|Inf |4.6347290| 2.793208009|-0.63598877|
## +--------------+---+---+----+---------+------------+-----------+
## |ground_veg    |No |147|Inf |1.2791962|-1.319602987|-2.73002911|
## |              |Yes|382|Inf |3.1973116| 0.447092148|-1.70011488|
## +--------------+---+---+----+---------+------------+-----------+
## |scars         |No |320|Inf |1.7844867|-1.366876275|-4.36944785|
## |              |Yes|209|Inf |5.3375381| 2.990719732|-0.81785066|
## +--------------+---+---+----+---------+------------+-----------+
## |conc_rainfall |No | 14|Inf |0.0000000|        -Inf|       -Inf|
## |              |Yes|515|Inf |2.4744353| 0.050496164|-1.88305089|
## +--------------+---+---+----+---------+------------+-----------+
## |wastewater    |No |206|Inf |1.6933194|-0.433864583|-2.78295151|
## |              |Yes|323|Inf |3.0220496| 0.267843730|-1.56189697|
## +--------------+---+---+----+---------+------------+-----------+
## |banana        |No |360|Inf |1.9977017|-0.279584862|-2.19722458|
## |              |Yes|169|Inf |3.7196511| 0.597003320|-1.45424502|
## +--------------+---+---+----+---------+------------+-----------+
## |drainage      |Y  | 62|Inf |0.7419373|-2.061423036|       -Inf|
## |              |P  |237|Inf |2.3841651|-0.508575904|-2.69462718|
## |              |N  |230|Inf |3.4612616| 0.996829594|-1.20585782|
## +--------------+---+---+----+---------+------------+-----------+
## |Overall       |   |529|Inf |2.3277965|-0.003780723|-1.91389034|
## +--------------+---+---+----+---------+------------+-----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.6, cex.axis=0.6, cex.sub=0.6)

Equation 4

p-value equation 3 > 0.05 (banana, DepTaludeCorte)
consider proportion
paremeters okay & so/so
fashion order

f4 <- lrm(risk ~ building + EN
+ DepEncNatural
+ crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + drainage + TC_mature_Soil + TC + +ground_veg
,data=train.data, x=TRUE , y=TRUE) # x=TRUE, y=TRUE used by resid() below #print (f4 , latex =TRUE , coefs =5) stargazer(anova(f4), type=“text”, style=“default”)

eq_OLR_04 <- polr(risk~ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
                  , data= train.data
           ,  method = "logistic", Hess = TRUE)
p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value

ctable <- coef(summary(eq_OLR_04))

ctable <- cbind(ctable, "p value" = p )

## Warning in cbind(ctable, `p value` = p): number of rows of result is not a
## multiple of vector length (arg 2)

ctable

##                          Value Std. Error     t value      p value
## woodTRUE            0.83582453  0.3231536  2.58646200 4.661278e-03
## TC_mature_soilTRUE  0.46928079  0.2159863  2.17273418 1.750680e-02
## T_constructionTRUE  0.64785978  0.2319182  2.79348381 8.342642e-03
## crackTRUE           2.03595987  0.3264925  6.23585457 3.820442e-01
## leaning_wallTRUE    1.88873561  0.5349995  3.53035028 2.204199e-10
## treeTRUE           -0.29669085  0.2320897 -1.27834547 2.098852e-04
## downward_floorTRUE  1.18185782  0.3512132  3.36507224 9.955513e-02
## tiltedTRUE          1.05561736  0.3045309  3.46637205 3.652902e-04
## ground_vegTRUE      0.80607603  0.2691129  2.99530772 2.694786e-04
## scarsTRUE           3.56915888  0.3321841 10.74452208 1.309370e-03
## conc_rainfallTRUE   0.70799658  0.6099675  1.16071204 3.246780e-27
## wastewaterTRUE      0.47604367  0.2301064  2.06879773 1.201501e-01
## bananaTRUE          0.24232550  0.2467204  0.98218659 2.383976e-02
## drainage.L          0.96265184  0.2782075  3.46019415 1.638219e-01
## drainage.Q         -0.11456294  0.1850794 -0.61899358 2.623306e-04
## R1|R2              -0.01715554  0.5933297 -0.02891402 2.721514e-01
## R2|R3               4.23008197  0.6468705  6.53930270 4.904554e-01
## R3|R4               9.12275834  0.7621375 11.96996394 3.021775e-11

stargazer((ctable), type="text", style="default", digits=2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## woodTRUE           0.84     0.32     2.59    0.005 
## TC_mature_soilTRUE 0.47     0.22     2.17    0.02  
## T_constructionTRUE 0.65     0.23     2.79    0.01  
## crackTRUE          2.04     0.33     6.24    0.38  
## leaning_wallTRUE   1.89     0.53     3.53      0   
## treeTRUE           -0.30    0.23     -1.28  0.0002 
## downward_floorTRUE 1.18     0.35     3.37    0.10  
## tiltedTRUE         1.06     0.30     3.47   0.0004 
## ground_vegTRUE     0.81     0.27     3.00   0.0003 
## scarsTRUE          3.57     0.33     10.74   0.001 
## conc_rainfallTRUE  0.71     0.61     1.16      0   
## wastewaterTRUE     0.48     0.23     2.07    0.12  
## bananaTRUE         0.24     0.25     0.98    0.02  
## drainage.L         0.96     0.28     3.46    0.16  
## drainage.Q         -0.11    0.19     -0.62  0.0003 
## R1| R2             -0.02    0.59     -0.03   0.27  
## R2| R3             4.23     0.65     6.54    0.49  
## R3| R4             9.12     0.76     11.97     0   
## ---------------------------------------------------

par(mfrow=c(4,4))
plot.xmean.ordinaly (risk ~  wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
          ,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+---------+------------+-----------+
## |              |   |N  |y>=1|y>=2     |y>=3        |y>=4       |
## +--------------+---+---+----+---------+------------+-----------+
## |wood          |No |453|Inf |2.2046047|-0.190418767|-2.25495704|
## |              |Yes| 76|Inf |3.6109179| 1.244324100|-0.71294981|
## +--------------+---+---+----+---------+------------+-----------+
## |TC_mature_soil|No |251|Inf |1.9596403|-0.248279525|-2.15800386|
## |              |Yes|278|Inf |2.7957558| 0.216671037|-1.72616219|
## +--------------+---+---+----+---------+------------+-----------+
## |T_construction|No |213|Inf |1.5595661|-0.867500568|-2.81839826|
## |              |Yes|316|Inf |3.4210000| 0.558372780|-1.53532994|
## +--------------+---+---+----+---------+------------+-----------+
## |crack         |No |444|Inf |2.1578106|-0.345745873|-2.73724936|
## |              |Yes| 85|Inf |4.4308168| 2.772588722|-0.07061757|
## +--------------+---+---+----+---------+------------+-----------+
## |leaning_wall  |No |499|Inf |2.2635346|-0.108322227|-2.15131488|
## |              |Yes| 30|Inf |      Inf| 2.639057330| 0.13353139|
## +--------------+---+---+----+---------+------------+-----------+
## |tree          |No |214|Inf |1.7754989|-0.515813165|-2.16645292|
## |              |Yes|315|Inf |2.9278549| 0.339738434|-1.76606998|
## +--------------+---+---+----+---------+------------+-----------+
## |downward_floor|No |467|Inf |2.1901071|-0.262743124|-2.26318255|
## |              |Yes| 62|Inf |      Inf| 4.110873864|-0.45953233|
## +--------------+---+---+----+---------+------------+-----------+
## |tilted        |No |425|Inf |2.1088948|-0.444840273|-2.50807371|
## |              |Yes|104|Inf |4.6347290| 2.793208009|-0.63598877|
## +--------------+---+---+----+---------+------------+-----------+
## |ground_veg    |No |147|Inf |1.2791962|-1.319602987|-2.73002911|
## |              |Yes|382|Inf |3.1973116| 0.447092148|-1.70011488|
## +--------------+---+---+----+---------+------------+-----------+
## |scars         |No |320|Inf |1.7844867|-1.366876275|-4.36944785|
## |              |Yes|209|Inf |5.3375381| 2.990719732|-0.81785066|
## +--------------+---+---+----+---------+------------+-----------+
## |conc_rainfall |No | 14|Inf |0.0000000|        -Inf|       -Inf|
## |              |Yes|515|Inf |2.4744353| 0.050496164|-1.88305089|
## +--------------+---+---+----+---------+------------+-----------+
## |wastewater    |No |206|Inf |1.6933194|-0.433864583|-2.78295151|
## |              |Yes|323|Inf |3.0220496| 0.267843730|-1.56189697|
## +--------------+---+---+----+---------+------------+-----------+
## |banana        |No |360|Inf |1.9977017|-0.279584862|-2.19722458|
## |              |Yes|169|Inf |3.7196511| 0.597003320|-1.45424502|
## +--------------+---+---+----+---------+------------+-----------+
## |drainage      |Y  | 62|Inf |0.7419373|-2.061423036|       -Inf|
## |              |P  |237|Inf |2.3841651|-0.508575904|-2.69462718|
## |              |N  |230|Inf |3.4612616| 0.996829594|-1.20585782|
## +--------------+---+---+----+---------+------------+-----------+
## |Overall       |   |529|Inf |2.3277965|-0.003780723|-1.91389034|
## +--------------+---+---+----+---------+------------+-----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)

Equation 5 - Based on Equation 1

based on Eq 1
less p-value > 0.10 (

# x=TRUE, y=TRUE used by resid() below 
#print (f1 , latex =TRUE , coefs =5)
#stargazer(anova(f1), type="text", style="default")

eq_OLR_05 <- polr(risk ~ brick+ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg,  data= train.data
           ,  method = "logistic", Hess = TRUE)

ctable <- coef(summary(eq_OLR_05))

p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                          Value Std. Error     t value      p value
## brickTRUE          -0.68448293  0.4422895 -1.54759018 6.086050e-02
## woodTRUE            0.93495221  0.3213409  2.90953371 1.809842e-03
## TC_mature_soilTRUE  0.48843387  0.2128941  2.29425794 1.088784e-02
## T_constructionTRUE  0.62176979  0.2268605  2.74075858 3.064876e-03
## crackTRUE           2.03573573  0.3236759  6.28942692 1.593200e-10
## leaning_wallTRUE    1.80313709  0.5248861  3.43529192 2.959579e-04
## scarsTRUE           3.65259960  0.3299800 11.06915386 8.853226e-29
## downward_floorTRUE  1.28531252  0.3522235  3.64913884 1.315604e-04
## tiltedTRUE          1.15534958  0.3048233  3.79022705 7.525482e-05
## conc_rainfallTRUE   1.11130377  0.5776637  1.92379022 2.719044e-02
## wastewaterTRUE      0.64328397  0.2221182  2.89613373 1.888957e-03
## ground_vegTRUE      0.89686946  0.2410767  3.72026633 9.950640e-05
## R1|R2              -0.05287725  0.7128386 -0.07417844 4.704342e-01
## R2|R3               4.03681794  0.7653634  5.27438047 6.660265e-08
## R3|R4               8.94611755  0.8555459 10.45661940 6.832213e-26

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## brickTRUE          -0.68    0.44     -1.55   0.06  
## woodTRUE           0.93     0.32     2.91    0.002 
## TC_mature_soilTRUE 0.49     0.21     2.29    0.01  
## T_constructionTRUE 0.62     0.23     2.74    0.003 
## crackTRUE          2.04     0.32     6.29      0   
## leaning_wallTRUE   1.80     0.52     3.44   0.0003 
## scarsTRUE          3.65     0.33     11.07     0   
## downward_floorTRUE 1.29     0.35     3.65   0.0001 
## tiltedTRUE         1.16     0.30     3.79   0.0001 
## conc_rainfallTRUE  1.11     0.58     1.92    0.03  
## wastewaterTRUE     0.64     0.22     2.90    0.002 
## ground_vegTRUE     0.90     0.24     3.72   0.0001 
## R1| R2             -0.05    0.71     -0.07   0.47  
## R2| R3             4.04     0.77     5.27   0.0000 
## R3| R4             8.95     0.86     10.46     0   
## ---------------------------------------------------

par(mfrow=c(3,4))
plot.xmean.ordinaly (risk ~  brick+ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg
          ,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+--------+------------+-----------+
## |              |   |N  |y>=1|y>=2    |y>=3        |y>=4       |
## +--------------+---+---+----+--------+------------+-----------+
## |brick         |No | 32|Inf |2.708050| 1.272965676|-0.78845736|
## |              |Yes|497|Inf |2.307020|-0.076496033|-2.02405640|
## +--------------+---+---+----+--------+------------+-----------+
## |wood          |No |453|Inf |2.204605|-0.190418767|-2.25495704|
## |              |Yes| 76|Inf |3.610918| 1.244324100|-0.71294981|
## +--------------+---+---+----+--------+------------+-----------+
## |TC_mature_soil|No |251|Inf |1.959640|-0.248279525|-2.15800386|
## |              |Yes|278|Inf |2.795756| 0.216671037|-1.72616219|
## +--------------+---+---+----+--------+------------+-----------+
## |T_construction|No |213|Inf |1.559566|-0.867500568|-2.81839826|
## |              |Yes|316|Inf |3.421000| 0.558372780|-1.53532994|
## +--------------+---+---+----+--------+------------+-----------+
## |crack         |No |444|Inf |2.157811|-0.345745873|-2.73724936|
## |              |Yes| 85|Inf |4.430817| 2.772588722|-0.07061757|
## +--------------+---+---+----+--------+------------+-----------+
## |leaning_wall  |No |499|Inf |2.263535|-0.108322227|-2.15131488|
## |              |Yes| 30|Inf |     Inf| 2.639057330| 0.13353139|
## +--------------+---+---+----+--------+------------+-----------+
## |scars         |No |320|Inf |1.784487|-1.366876275|-4.36944785|
## |              |Yes|209|Inf |5.337538| 2.990719732|-0.81785066|
## +--------------+---+---+----+--------+------------+-----------+
## |downward_floor|No |467|Inf |2.190107|-0.262743124|-2.26318255|
## |              |Yes| 62|Inf |     Inf| 4.110873864|-0.45953233|
## +--------------+---+---+----+--------+------------+-----------+
## |tilted        |No |425|Inf |2.108895|-0.444840273|-2.50807371|
## |              |Yes|104|Inf |4.634729| 2.793208009|-0.63598877|
## +--------------+---+---+----+--------+------------+-----------+
## |conc_rainfall |No | 14|Inf |0.000000|        -Inf|       -Inf|
## |              |Yes|515|Inf |2.474435| 0.050496164|-1.88305089|
## +--------------+---+---+----+--------+------------+-----------+
## |wastewater    |No |206|Inf |1.693319|-0.433864583|-2.78295151|
## |              |Yes|323|Inf |3.022050| 0.267843730|-1.56189697|
## +--------------+---+---+----+--------+------------+-----------+
## |ground_veg    |No |147|Inf |1.279196|-1.319602987|-2.73002911|
## |              |Yes|382|Inf |3.197312| 0.447092148|-1.70011488|
## +--------------+---+---+----+--------+------------+-----------+
## |Overall       |   |529|Inf |2.327797|-0.003780723|-1.91389034|
## +--------------+---+---+----+--------+------------+-----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)

OLR Equation 6

# x=TRUE, y=TRUE used by resid() below 
#print (f1 , latex =TRUE , coefs =5)
#stargazer(anova(f1), type="text", style="default")

eq_OLR_06 <- polr(risk ~ brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana,  data= train.data
           ,  method = "logistic", Hess = TRUE)

ctable <- coef(summary(eq_OLR_06))

p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                          Value Std. Error     t value      p value
## brickTRUE          -0.93931481  0.5397329 -1.74033282 4.090030e-02
## woodTRUE            0.66136414  0.3259215  2.02921291 2.121830e-02
## mixedTRUE          -0.03099172  0.4900748 -0.06323875 4.747882e-01
## ENTRUE              0.52965497  0.4008171  1.32143806 9.317767e-02
## TCTRUE             -1.00812087  0.5132688 -1.96411881 2.475816e-02
## T_constructionTRUE  0.50301570  0.3577433  1.40608018 7.985016e-02
## landfillTRUE        0.12753941  0.3220020  0.39608266 3.460220e-01
## leakTRUE            0.01331656  0.2261086  0.05889456 4.765180e-01
## garbageTRUE         0.18355883  0.2897499  0.63350774 2.632011e-01
## crackTRUE           2.10415748  0.3286008  6.40338537 7.598448e-11
## leaning_wallTRUE    1.84423156  0.5374334  3.43155354 3.000673e-04
## treeTRUE           -0.24615528  0.2348731 -1.04803510 1.473112e-01
## tiltedTRUE          1.20683744  0.3120180  3.86784548 5.490060e-05
## angleD              0.23882256  0.4645472  0.51409749 3.035919e-01
## angleE              0.78920419  0.5435980  1.45181586 7.327641e-02
## ground_vegTRUE      0.91594576  0.2670198  3.43025458 3.015076e-04
## scarsTRUE           3.74718317  0.3338173 11.22525095 1.532575e-29
## conc_rainfallTRUE   1.44252931  0.5807357  2.48396892 6.496357e-03
## wastewaterTRUE      0.56442413  0.2286149  2.46888647 6.776711e-03
## bananaTRUE          0.32338244  0.2510756  1.28798832 9.887502e-02
## R1|R2              -0.48612219  1.1240503 -0.43247370 3.326986e-01
## R2|R3               3.53865419  1.1462990  3.08702534 1.010852e-03
## R3|R4               8.37823450  1.2133569  6.90500441 2.510093e-12

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## brickTRUE          -0.94    0.54     -1.74   0.04  
## woodTRUE           0.66     0.33     2.03    0.02  
## mixedTRUE          -0.03    0.49     -0.06   0.47  
## ENTRUE             0.53     0.40     1.32    0.09  
## TCTRUE             -1.01    0.51     -1.96   0.02  
## T_constructionTRUE 0.50     0.36     1.41    0.08  
## landfillTRUE       0.13     0.32     0.40    0.35  
## leakTRUE           0.01     0.23     0.06    0.48  
## garbageTRUE        0.18     0.29     0.63    0.26  
## crackTRUE          2.10     0.33     6.40      0   
## leaning_wallTRUE   1.84     0.54     3.43   0.0003 
## treeTRUE           -0.25    0.23     -1.05   0.15  
## tiltedTRUE         1.21     0.31     3.87   0.0001 
## angleD             0.24     0.46     0.51    0.30  
## angleE             0.79     0.54     1.45    0.07  
## ground_vegTRUE     0.92     0.27     3.43   0.0003 
## scarsTRUE          3.75     0.33     11.23     0   
## conc_rainfallTRUE  1.44     0.58     2.48    0.01  
## wastewaterTRUE     0.56     0.23     2.47    0.01  
## bananaTRUE         0.32     0.25     1.29    0.10  
## R1| R2             -0.49    1.12     -0.43   0.33  
## R2| R3             3.54     1.15     3.09    0.001 
## R3| R4             8.38     1.21     6.91      0   
## ---------------------------------------------------

par(mfrow=c(5,4))
plot.xmean.ordinaly (risk ~  brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana
          ,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+--------+------------+-----------+
## |              |   |N  |y>=1|y>=2    |y>=3        |y>=4       |
## +--------------+---+---+----+--------+------------+-----------+
## |brick         |No | 32|Inf |2.708050| 1.272965676|-0.78845736|
## |              |Yes|497|Inf |2.307020|-0.076496033|-2.02405640|
## +--------------+---+---+----+--------+------------+-----------+
## |wood          |No |453|Inf |2.204605|-0.190418767|-2.25495704|
## |              |Yes| 76|Inf |3.610918| 1.244324100|-0.71294981|
## +--------------+---+---+----+--------+------------+-----------+
## |mixed         |No |490|Inf |2.291412|-0.073502462|-2.00798258|
## |              |Yes| 39|Inf |2.917771| 0.934309237|-1.06471074|
## +--------------+---+---+----+--------+------------+-----------+
## |EN            |No |348|Inf |1.932838|-0.492476485|-2.39789527|
## |              |Yes|181|Inf |4.083171| 0.990981624|-1.29226541|
## +--------------+---+---+----+--------+------------+-----------+
## |TC            |No | 26|Inf |     Inf| 1.203972804|-1.20397280|
## |              |Yes|503|Inf |2.272345|-0.059659836|-1.96191049|
## +--------------+---+---+----+--------+------------+-----------+
## |T_construction|No |213|Inf |1.559566|-0.867500568|-2.81839826|
## |              |Yes|316|Inf |3.421000| 0.558372780|-1.53532994|
## +--------------+---+---+----+--------+------------+-----------+
## |landfill      |No |325|Inf |1.828127|-0.509184934|-2.52905643|
## |              |Yes|204|Inf |4.615121| 0.828692673|-1.29098418|
## +--------------+---+---+----+--------+------------+-----------+
## |leak          |No |336|Inf |2.001480|-0.251314428|-2.32238772|
## |              |Yes|193|Inf |3.279837| 0.431440595|-1.40583896|
## +--------------+---+---+----+--------+------------+-----------+
## |garbage       |No |347|Inf |2.066538|-0.272568435|-2.28683674|
## |              |Yes|182|Inf |3.079614| 0.516690743|-1.40008768|
## +--------------+---+---+----+--------+------------+-----------+
## |crack         |No |444|Inf |2.157811|-0.345745873|-2.73724936|
## |              |Yes| 85|Inf |4.430817| 2.772588722|-0.07061757|
## +--------------+---+---+----+--------+------------+-----------+
## |leaning_wall  |No |499|Inf |2.263535|-0.108322227|-2.15131488|
## |              |Yes| 30|Inf |     Inf| 2.639057330| 0.13353139|
## +--------------+---+---+----+--------+------------+-----------+
## |tree          |No |214|Inf |1.775499|-0.515813165|-2.16645292|
## |              |Yes|315|Inf |2.927855| 0.339738434|-1.76606998|
## +--------------+---+---+----+--------+------------+-----------+
## |tilted        |No |425|Inf |2.108895|-0.444840273|-2.50807371|
## |              |Yes|104|Inf |4.634729| 2.793208009|-0.63598877|
## +--------------+---+---+----+--------+------------+-----------+
## |angle         |C  | 32|Inf |     Inf|-0.379489622|-3.43398720|
## |              |D  |118|Inf |3.646320| 1.167605160|-1.31372367|
## |              |E  |379|Inf |2.029941|-0.303090698|-2.08241331|
## +--------------+---+---+----+--------+------------+-----------+
## |ground_veg    |No |147|Inf |1.279196|-1.319602987|-2.73002911|
## |              |Yes|382|Inf |3.197312| 0.447092148|-1.70011488|
## +--------------+---+---+----+--------+------------+-----------+
## |scars         |No |320|Inf |1.784487|-1.366876275|-4.36944785|
## |              |Yes|209|Inf |5.337538| 2.990719732|-0.81785066|
## +--------------+---+---+----+--------+------------+-----------+
## |conc_rainfall |No | 14|Inf |0.000000|        -Inf|       -Inf|
## |              |Yes|515|Inf |2.474435| 0.050496164|-1.88305089|
## +--------------+---+---+----+--------+------------+-----------+
## |wastewater    |No |206|Inf |1.693319|-0.433864583|-2.78295151|
## |              |Yes|323|Inf |3.022050| 0.267843730|-1.56189697|
## +--------------+---+---+----+--------+------------+-----------+
## |banana        |No |360|Inf |1.997702|-0.279584862|-2.19722458|
## |              |Yes|169|Inf |3.719651| 0.597003320|-1.45424502|
## +--------------+---+---+----+--------+------------+-----------+
## |Overall       |   |529|Inf |2.327797|-0.003780723|-1.91389034|
## +--------------+---+---+----+--------+------------+-----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)

Predicion on test data Eq 1: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel1 <- predict(eq_OLR_01, test.data) # predict the levels directly

predictedScores1 <- predict(eq_OLR_01, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel1)

##     predictedLevel1
##      R1 R2 R3 R4
##   R1  5 14  0  0
##   R2  1 88  4  0
##   R3  0 20 54 10
##   R4  0  0 16 12

p1 <- mean(as.character(test.data$risk) != as.character(predictedLevel1)) #misclassification error
p1

## [1] 0.2901786

Predicion on test data Eq 2: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel2 <- predict(eq_OLR_02, test.data) # predict the levels directly

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel2)

##     predictedLevel2
##      R1 R2 R3 R4
##   R1  6 13  0  0
##   R2  3 83  7  0
##   R3  0 20 55  9
##   R4  0  0 16 12

p2 <- mean(as.character(test.data$risk) != as.character(predictedLevel2))
p2

## [1] 0.3035714

Predicion on test data Eq 3: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel3 <- predict(eq_OLR_03, test.data) # predict the levels directly

predictedScores1 <- predict(eq_OLR_03, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel3)

##     predictedLevel3
##      R1 R2 R3 R4
##   R1  5 14  0  0
##   R2  3 86  4  0
##   R3  0 20 55  9
##   R4  0  0 16 12

p3 <- mean(as.character(test.data$risk) != as.character(predictedLevel3))
p3

## [1] 0.2946429

Predicion on test data Eq 4: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel4 <- predict(eq_OLR_04, test.data) # predict the levels directly

predictedScores1 <- predict(eq_OLR_04, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel4)

##     predictedLevel4
##      R1 R2 R3 R4
##   R1  5 14  0  0
##   R2  3 86  4  0
##   R3  0 20 55  9
##   R4  0  0 16 12

p4 <- mean(as.character(test.data$risk) != as.character(predictedLevel4))
p4

## [1] 0.2946429

Predicion on test data Eq 5: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel5 <- predict(eq_OLR_05, test.data) # predict the levels directly

predictedScores5 <- predict(eq_OLR_05, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel5)

##     predictedLevel5
##      R1 R2 R3 R4
##   R1  4 15  0  0
##   R2  1 88  4  0
##   R3  0 20 55  9
##   R4  0  0 16 12

p5 <- mean(as.character(test.data$risk) != as.character(predictedLevel5))
p5

## [1] 0.2901786

Predicion on test data Eq 6: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel6 <- predict(eq_OLR_06, test.data) # predict the levels directly

predictedScores6 <- predict(eq_OLR_06, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel6)

##     predictedLevel6
##      R1 R2 R3 R4
##   R1  6 13  0  0
##   R2  2 86  5  0
##   R3  0 21 53 10
##   R4  0  1 13 14

p6 <- mean(as.character(test.data$risk) != as.character(predictedLevel6))
p6

## [1] 0.2901786

Predicion on test data Eq 7: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

#Table 

df2 <- data.frame(
  
  "Equations"=c(1:6), 
  "Predicted"=c(1-p1, 
                1-p2,
                1-p3,
                1-p4,
                1-p5,
                1-p6
               
              
    
    
  )
  
  
  
)

df2

##   Equations Predicted
## 1         1 0.7098214
## 2         2 0.6964286
## 3         3 0.7053571
## 4         4 0.7053571
## 5         5 0.7098214
## 6         6 0.7098214

Diagnostic - one model

Erica Goto

2/10/2019

Library & Dataset

Inspect Dataset Using Training and Validation

OLR Equations

Inspect Dataset Using Training and Validation

Equation 1

Creating function with four level

Equation 2

Equation 3

Equation 4

Equation 5 - Based on Equation 1

OLR Equation 6

Predicion on test data Eq 1: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 2: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 3: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 4: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 5: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 6: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 7: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html